Soft Field Tomography Mesh Refinement for Resolution
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Solution Overview
Problem
Soft field tomography systems face limitations in resolution due to the number of sensing elements, as increasing their number reduces signal-to-noise ratio and makes the system bulky and expensive, while adding more elements is necessary for improved image resolution.
Innovation Solution
An iterative method that defines a first mesh of an object, applies an excitation, computes a response, and updates a subset of nodes to form an updated mesh, maintaining connectivity relationships, allowing for improved property distribution computation without increasing the number of sensing elements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the number of sensing elements is increased to improve image resolution, then the resolution is improved, but the signal-to-noise ratio drops and the system becomes bulky and expensive
Solution Approach 1:
The patent applies segmentation by dividing the mesh into multiple levels of refinement. Instead of uniformly increasing the number of sensing elements, the mesh is segmented into regions of interest that receive higher resolution treatment. This allows concentrated computational resources on critical areas while maintaining a manageable overall system complexity.
Solution Approach 2:
The patent implements local quality by applying different mesh densities to different regions. Regions of interest undergo iterative mesh refinement where nodes are updated and repositioned to achieve higher local resolution. This non-uniform approach improves measurement precision in critical areas without requiring a proportional increase in the total number of sensing elements system-wide.
2Measurement precision
If the number of sensing elements is increased to improve image resolution, then the resolution is improved, but the system becomes bulky and expensive
Solution Approach 1:
The mesh is segmented into refinement levels, allowing high resolution only where needed. This segmentation strategy enables the system to achieve improved image resolution in regions of interest without proportionally increasing the overall system volume, as the refined mesh occupies only a portion of the total measurement space.
Solution Approach 2:
By applying local mesh refinement to specific regions rather than uniformly across the entire object, the system achieves higher resolution where necessary while maintaining a compact overall structure. The updated nodes are concentrated in regions of interest, reducing the need for a bulky system configuration.
3Measurement precision
If the number of sensing elements is increased to improve image resolution, then the resolution is improved, but the cost increases
Solution Approach 1:
The computational mesh is segmented into refinement zones, allowing the system to achieve high resolution imaging without requiring a correspondingly high number of physical sensing elements. This segmentation approach reduces manufacturing costs by concentrating measurement capabilities only where high resolution is critical.
Solution Approach 2:
The patent applies local quality enhancement through iterative mesh updates in regions of interest. This approach improves image resolution in critical areas using a limited number of sensing elements, thereby reducing the overall system cost compared to uniformly high-resolution systems that would require many more elements.
4Measurement precision
If mesh refinement is applied uniformly across the object, then the overall resolution is improved, but the computational complexity increases significantly
Solution Approach 1:
The computational domain is segmented into regions of interest and non-critical regions. Mesh refinement is applied selectively to regions of interest through iterative node updates, while other areas maintain coarser mesh density. This segmentation reduces computational complexity compared to uniform refinement while maintaining improved spatial resolution where needed.
Solution Approach 2:
The patent implements local quality enhancement by updating only the nodes in regions of interest during iterative refinement. This localized approach improves spatial resolution in critical areas without proportionally increasing computational complexity across the entire object, as the refined calculation is confined to specific regions rather than applied uniformly everywhere.
Data Source
AI summary
An iteration method for computing a distribution of one or more properties within an object comprises defining a first mesh of the object, applying an excitation to the object, computing a response of the object to the applied excitation, obtaining a reference response of the object corresponding to the applied excitation, computing a distribution of one or more properties of the object, and updating at least a subset of the nodes of the first mesh to form an updated mesh of the object. The distribution of one or more properties of the object is computed using the computed response, the reference response, and the first mesh. The first mesh includes a plurality of nodes and elements. A connectivity relationship of the subset of the nodes in the updated mesh remains the same as in the first mesh.